臺灣博碩士論文加值系統

在無線通訊傳輸中，由於傳輸品質的提升，使得傳輸資料量日漸增大，因此以往一般的傳輸媒介已無法負荷如此龐大的資料量，所以光纖通訊漸漸成為基本的傳輸媒介。由於無線傳輸環境中，會因為傳輸媒介或通道環境而產生錯誤資訊，以光纖通訊來說，光的散射就是其主要干擾。因此利用錯誤更正碼來修正通訊的可靠度的方法是不可或缺的重要一環。而渦輪碼用於遞迴式系統上已被證實對通道所造成的錯誤與干擾訊息有重大的改善。而在眾多的渦輪碼解碼演算法中，Log-MAP解碼演算法屬於複雜度較低，卻又有著較佳的效能，諸如：MAP、Max-Log-MAP、SOVA解碼演算法。

近年來多輸入多輸出系統被廣泛的運用在行動通訊研究中，多輸入多輸出系統可以有效的增加通道容量，並且可以大幅的降低傳輸錯誤機率。而通常在模擬多輸入多輸出系統中，都是使用蒙特．卡羅定理的方式來觀察系統效能，但是當傳輸速率很低時，不容易使用蒙特．卡羅定理的方式來實現，因此本篇論文考慮在萊斯衰落通道時，利用重要取樣定理的方法來觀察多天線系統在天線數目改變時候和低傳輸速率時候的系統效能，並且探討多樣性-空間多工轉移之間的關係。另外本論文也透過模擬分析多天線系統的中斷機率和通道容量。

In communications, due to the demand of transmitting multimedia data over the link, the transmission rate has been so high that conventional media can no longer accommodate. As a result, optical communication has become the major support for this kind of multimedia transmission. Similar to wireless communication where the major impairment of signals comes from the multipath effect, in optical communication light can be distorted due to dispersion. Thus, using error correcting codes to improve the link quality of optical communication is prominent. Iterative decoding based on turbo codes has been shown to be able to improve significantly the link performance of a noisy interference channel. In this thesis, we will investigate the performance of turbo code when it is used for optical communication with hard output.

Multiple-Input Multiple-Output(MIMO) system can be used for increasing the channel capacity and decreasing the error probability of transmission in a mobile environment. In general, error performances of MIMO systems can be simulated by using Monte Carlo method. However, at high signal-to-noise ratio regime or at low transmission rate, the corresponding error probability is usually extremely small and is hard to be observed by Monte Carlo methods. In this work, importance sample method is used to investigate the performance of MIMO systems with various numbers of antennas. The simulation results are compared with theoretical DMT (Diversity-Multiplexing Tradeoff) analysis. Outage probability and channel capacity of MIMO systems are also simulated.

誌謝辭.....................................i
中文摘要..................................ii
英文摘要.................................iii
目錄.......................................v
圖目錄...................................vii
表目錄....................................ix
第一章緒論.................................1
第二章渦輪碼編碼原理.......................6
2.1遞迴式系統迴旋碼........................6
2.2渦輪碼編碼架構..........................9
2.3渦輪碼解碼.............................11
2.4MAP與Log-MAP法則.......................15
2.5渦輪碼效能分析.........................24
第三章模擬結果與分析......................26
第四章結論................................29
第五章緒論................................30
第六章通道容量............................33
6.1多樣性與空間多工.......................33
6.2萊斯衰落通道...........................36
第七章蒙特卡羅與重要取樣..................43
7.1蒙特．卡羅定理.........................43
7.2重要取樣定理...........................46
第八章模擬結果與分析......................50
第九章結論................................57
參考文獻..................................58
作者簡介..................................61

[1] WAN H. KIM, and CHARLES V. FREIMAN, “Single Error-Correcting Codes for Asynnetric Binary Channels” IRE Trans. Inform. Theory, vol. IT-1, pp. 62-66, June.
[2] Beooru, C, Glavieux, A., and Thitimajshima, P., “Near Shannon limit error-correcting coding and decoding: Turbo-codes” IEEE International Conference on Communications, ICC’93, Geneva. Vol. 2, 1993, pp. 1064 – 1070.
[3] L. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “Optimal decoding of linear codes for minimizing symbol error rate,” IEEE Trans. Inform. Theory, vol. IT-20, pp. -284 – 287, March 1974.
[4] J.Vogt, K. Koora, A.Finger and G. Fettweis, “Comparison of Different Turbo Decoder Realizations for IMT-2000,” GLOBECOM 99, pp. 2704 – 2708, Dec. 1999.
[5] Forney, G. D., Concatenated codes, Cambridge, Massachusetts: M.I.T Press, 1996.
[6] A. J. Viterbi, “Error bounds for convolutional codes and an asymptotically optimum decoding algorithm,” IEEE Trans. Inform. Theory, vol. IT-13, pp. 260 – 269, Apr. 1967.
[7] A. J. Viterbi and J. K. Omura. “Principles of digital communication and coding. New York”: MacGraw-Hill, 1979.
[8] J. B. Cain, G. C. Clark, and J. M. Geist, “Punctured convolutional codes of rate (n-1)/n and simplified maximum likelihood decoding,” IEEE Trans. Inform. Theory, vol. IT-25, pp. 97 – 100, Jan 1979.
[9] E. Dunscombe and F. C. Piper, “Optimal interleaving scheme for convolutional coding,” Electron. Lett., vol. 25, no. 22, pp. 1517 – 1518, Oct. 1989.
[10] Hagenauer, J. “Iterative Decoding of Binary Block and Convolutional Codes,” IEEE Trans. On Information Theory, vol. 42, on. 2, March 1996, pp. 429 – 278, Mar. 1973.
[11] Robertson, P., Villebrun, E., and Hoeher, P. “A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain,” IEEE International Conference on Communications, ICC ’95, Seattle, 1955, vol. 2, 1955, pp. 1009 – 1013.
[12] Forney, G. D., “The Viterbi algorithm,” Proc. IEEE, Vol. 61, pp. 268 – 278 Mar. 1973.
[13] H. Shen and A. Ghrayeb, “Analysis of the outage probability for MIMO systems with receive antenna selection,” IEEE Trans. Veh. Technol., vol.55, pp. 1435–1441, July 2006.
[14] L. Zheng and D. N. C. Tse, “Diversity and multiplexing: A fundamental tradeoff in multiple antenna channels, ” IEEE Trans. Inf. Theory, vol. 49, no. 5, pp. 1073–1096, May 2003.
[15] G. J. Foschini and M. J. Gans, “On limits of wireless communication in a fading environment when using multiple antennas,” Wireless Personal Commun., vol. 6, no. 3, pp. 311–335, Mar. 1998.
[16] I. E. Telatar, “Capacity of multi-antenna Gaussian channels,” Eur. Trans.Telecom., vol. 10, no. 6, pp. 585–595, Nov. 1999.
[17] F. R. Farrokhi, G. J. Foschini, A. Lozano, and R. Valenzuela, “Linkoptimal space-time processing with multiple transmit and receive antennas,” IEEE Commun. Lett., vol. 5, no. 3, pp. 85–87, Mar. 2001.
[18] A. T. James, “Distributions of matrix variates and latent roots derived from normal samples,” Ann. Math. Statist., vol. 35, no. 1, pp. 475–501, 1964.
[19] K. I. Gross and D. S. P. Richards, “Total positivity, spherical series, and hypergeometric functions of matrix argument,” Journal of Approximation Theory, vol. 59, pp. 224–246, 1989.
[20] J. Spanier and K. B. Oldham, Eds., An Atlas of Functions. NewYork : HemispherePublishing Corporation, 1987.
[21] R. A. Fisher, “The general sampling distribution of the multiple correlation coefficient,” in Proc. Roy. Soc. London Ser. A, vol. 121, 1928, pp. 654–673.
[22] N.Metropolis, "The Beginning of the Monte Carlo Method." Los Alamos Science, No. 15, p. 125., 1987
[23] Mark Danny, “Introduction to importance sampling in rare-event simulations”, European Journal of Physics, Volume 22, Number 4 , pp. 403-411(9), 2001